Abstract #2253
Tumour relapse prediction using multi-parametric MR data recorded during follow-up of GBM patients
Adrian Ion-Margineanu 1,2 , Sofie Van Cauter 3 , Diana M Sima 1,2 , Frederik Maes 2,4 , Stefaan W Van Gool 5 , Stefaan Sunaert 3 , Uwe Himmelreich 6 , and Sabine Van Huffel 1,2
1
STADIUS, KU Leuven - ESAT, Leuven, Belgium,
Belgium,
2
iMinds
Medical IT, Leuven, Belgium,
3
Department
of Radiology, University Hospitals of Leuven, Leuven,
Belgium,
4
PSI, KU Leuven - ESAT, Belgium,
5
Department
of Pedriatic Neuro-oncology, University Hospitals of
Leuven, Belgium,
6
Department
of Imaging and Pathology - Biomedical MRI/ MoSAIC, KU
Leuven, Belgium
Our study is trying to find a relation between
multi-parametric MR data (T1 post contrast - MRI, T2* -
MRI, FLAIR, Perfusion MRI, Diffusion MRI, MR
Spectroscopy) acquired during the follow-up of 29
glioblastoma multiforme (GBM) patients and the relapse
of the brain tumour after surgery, as described by the
clinically accepted RANO criteria. We find that ensemble
classifiers can accurately predict the outcome of the
therapy with approximately one month in advance before
doctors. The same results were found also when using
just perfusion features.
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